Following [1]}, we fine-tune a DenseNet-121 [2]} on the CXR14 data set.
The model was pre-trained on ImageNet pre-trained and is available on pytorch.org.
For fine-tuning, we replace the last layer with a fully-connected layer with 14 outputs, matching the 14 classes of the CXR14 data set.
The outputs are converted to a probability by applying the sigmoid function.
The class-wise predictive thresholds were determined by setting a chest pathology classification sensitivity of 95 % only on the CXR14 validation data set, without any OOD data.
We use binary cross entropy as a loss function and train the model using ADAM [3]} optimization with default parameters (\(\beta _1 = 0.9,\quad \beta _2 = 0.999\) ) and an initial learning rate of 0.0003.
We divide the learning rate by a factor of ten if the validation loss did not improve over the last two epochs.
We apply weight decay with a value of 0.0001.
We train the model for eight epochs and select the best model based on the validation loss.
The input images are resized to 256 x 256 pixels and normalized according to the ImageNet mean and standard deviation.
Then we apply 224 x 224 ten crop, i.e., we take crops from each corner and the centre of the image and repeat the process for the horizontally flipped image: producing ten 224 x 224 pixel images per sample.
The model predictions of the ten images are averaged before calculating the loss.
